%load_ext pretty_jupyter

import warnings
warnings.filterwarnings('ignore')

import pandas as pd
import country_converter as coco
import seaborn as sns
import matplotlib.pyplot as plt
import geopandas as gpd
import numpy as np
import plotly.express as px

#load inital dataset
raw = pd.read_csv("life-expectancy.csv")

#define entity categories
continent = ['Africa', 'Asia', 'Europe', 'Latin America and the Caribbean', 'Northern America', 'Oceania']
income_groups = ['High-income countries', 'Upper-middle-income countries', 'Middle-income countries', 'Lower-middle-income countries', 'Low-income countries', 'No income group available']
development = ['More developed regions', 'Small Island Developing States (SIDS)', 'Less developed regions', 'Less developed regions, excluding China', 'Less developed regions, excluding least developed countries', 'Least developed countries', 'Land-locked Developing Countries (LLDC)']

#create new df for continents, income level, development status
continents = raw[raw['Entity'].isin(continent) == True]
income = raw[raw['Entity'].isin(income_groups) == True]
develop = raw[raw['Entity'].isin(development) == True]

#create new df for countries
countries = raw[raw['Entity'].isin(development) == False]
countries = countries[countries['Entity'].isin(income_groups) == False]
countries = countries[countries['Entity'].isin(continent) == False]

#add continent column for countries df
converter = coco.CountryConverter()
countries['Continent'] = converter.convert(names=countries['Code'], src="ISO3", to="continent")

#find countries without continent match - all European countries
a = countries[countries['Code'].isna() == True]
a = a[['Entity', 'Code']]
grouped = a.groupby(by="Entity").sum()

#update continent column
countries['Continent'] = countries['Continent'].str.replace('not found', 'Europe')

# load dataset (https://ghdx.healthdata.org/record/ihme-data/gbd-2021-cause-specific-mortality-1990-2021)
disease = pd.read_excel("disease-mortality-rates.XLSX")

#create a subset with only mortality rate per 100,000 people
disease = disease[['location_type', 'location_name', 'cause_name', '2010 (ASMR)', '2019 (ASMR)', '2020 (ASMR)', '2021 (ASMR)']]

#convert columns to numeric
cols = ['2010 (ASMR)', '2019 (ASMR)', '2020 (ASMR)', '2021 (ASMR)']
for i in cols:
    disease[i] = disease.loc[:, i].str.replace(r"\(.*\)","", regex=True)
    disease[i] = pd.to_numeric(disease.loc[:, i])
    
#create column for change from 2010 to 2021
disease.loc[:, 'change'] = (disease.loc[:, '2021 (ASMR)'] - disease.loc[:, '2010 (ASMR)'])

#create global dataset
globe = disease[disease['location_type'] == 'Global']

#create global grouped dataset by cause
globe_group = globe[['cause_name', '2010 (ASMR)', '2019 (ASMR)', '2020 (ASMR)', '2021 (ASMR)', 'change']].groupby(by='cause_name').mean().reset_index()

#create africa dataset
a = ['North Africa and Middle East', 'Central Sub-Saharan Africa', 'Eastern Sub-Saharan Africa', 'Southern Sub-Saharan Africa', 'Western Sub-Saharan Africa']
africa = disease[(disease['location_type'] == 'Region') & (disease['location_name'].isin(a) == True)]

#create grouped africa dataset by cause
ac = africa.copy()
ac = ac[['cause_name', '2010 (ASMR)', '2019 (ASMR)', '2020 (ASMR)', '2021 (ASMR)', 'change']]
africa_group = ac.groupby(by='cause_name').mean().reset_index()

#create column of difference between african vs global average in 2021
africa_group['diff'] = (africa_group['2021 (ASMR)'] - globe_group['2021 (ASMR)'])
africa_group = africa_group.sort_values(by='diff', ascending=False)

#load dataset (https://cloud.ihme.washington.edu/s/6w3TkFaQw63Djnd?)
decomp = pd.read_excel("life-expectancy-decomp.XLSX")

#create africa df
af = ['Western Sub-Saharan Africa', 'North Africa and Middle East', 'Central Sub-Saharan Africa', 'Southern Sub-Saharan Africa', 'Eastern Sub-Saharan Africa']
decomp_africa = decomp[decomp['Location Name'].isin(af) == True]

#filter 1990-2021 only
decomp_africa = decomp_africa[(decomp_africa['Start Year'] == 1990) & (decomp_africa['End Year'] == 2021)]
decomp_africa = decomp_africa.drop(axis=0, index=670)

#load df (Uppsala Conflict Data Program (2023); Natural Earth (2022) – processed by Our World in Data)
conflict = pd.read_csv("deaths-in-armed-conflicts.csv")

expense = pd.read_csv("health-expenditure.csv")

#load df
gdp = pd.read_csv("gdp.csv")
gdp = gdp.dropna()

#create country code column
converter = coco.CountryConverter()
gdp['iso_a3'] = converter.convert(names=gdp['Country '], to="ISO3")

#check which ones were missed - only regions, all country codes found
#gdp[gdp['iso_a3'] == 'not found']